Pose estimation of mobile devices is useful for a wide variety of applications, including augmented reality and geo-tagging. Even though most of today's cell phones are equipped with sensors such as GPS, accelerometers, and gyros, pose estimated via these is often inaccurate. This is particularly true in urban environments where tall buildings may block satellite view for GPS and distortions in Earth's magnetic field from power lines adversely affect compass readings. In addition, indoor environments whether urban or rural present similar issues with respect to GPS reception and distortions due to power lines and other structural components of a building.
In order to determine pose, most modern smart-phones are equipped with full sensor packages, including an accelerometer, gyros, GPS, and a compass. For example, an application such as YELP MONOCLE™ utilizes a phone's GPS and compass to overlay nearby entities such as shops and restaurants on the viewfinder. Unfortunately, in urban and indoor environments, tall buildings block satellite view and introduce multi-path effects, while power lines and trains add significant magnetic interference, making both GPS and compass readings error prone. Furthermore, a barometer, a source of altitude information, is extremely unreliable. Since most of today's mobile devices are equipped with camera sensors, it is conceivable to use camera imagery to compensate for the above deficiencies in recovering full position and orientation of the mobile device. As such, single image pose estimation of image capture devices is presented herein.